Exploring Climate Feedbacks and Variability in an Objective Tuning of a Climate Model of Intermediate Complexity

Clouds, like other small-scale Earth-system processes, have to be approximated by simple functions in climate models. Such parameterizations often include uncertain constants. These parameters are estimated in a procedure called tuning where the model output is optimized with respect to observations [1]. Most models are tuned against present-day reanalyses [1]. However, recent studies [2,3] have demonstrated that certain parameter values which produce climate states in good agreement with present-day observations, are not well-suited for simulating climate states very different from present-day, such as the substantially colder Last Glacial Maximum (LGM, about 21.000 years ago). This implies equifinality, in which case better parameter values may be identifiable, or state-dependency, which should be taken into account when the model is used to extrapolate beyond the range of observations. 

Here, we present a multi-state iterative Bayesian parameter estimation procedure. We use it to tune PaleoPlaSim [4], a coupled Atmosphere-Ocean General Circulation Model of intermediate complexity. It is a a paleoclimate-enhanced version of PlaSim-LSG [5]. We start by creating a Perturbed Parameter Ensemble (PPE). We vary 12 model parameters relating mainly to ocean mixing, cloud properties, and land surface properties. For each PPE member, we initialize a present-day (PD) and an LGM simulation. Across the initial PPE, we find that, globally, colder ensemble members exhibit a larger LGM-PD anomaly and higher temperature variability. This is consistent with palaeoclimate data and theoretical expectation. However, this relationship is weak and may be of opposing sign regionally, notably in the tropics. We hypothesize that this is due to different local climate feedback amplified or weakened by the perturbed parameters. This indicates that regional temperature variability is not necessarily fully coupled to global temperature, and climate sensitivity, indicated here by the LGM-PD anomaly.

To explore these degrees of freedom, we perform multiple tuning runs. We vary tuning targets, including weighted combinations of present-day observations, LGM climate reconstructions, and a temperature variability term. We test whether and how well this exploratory approach can identify state-dependent model parameters. Finally, we identify pathways to generalize our approach for complex climate model developments under computational constraints, for example by the use of machine-learning based emulators.

 

[1] Hourdin et al., “The Art and Science of Climate Model Tuning”, Bulletin of the American Meteorological Society, 98, 589–602, 2017.

[2] Sherriff-Tadano et al., “Southern Ocean Surface Temperatures and Cloud Biases in Climate Models Connected to the Representation of Glacial Deep Ocean Circulation”, Journal of Climate, 36, 3849–3866, 2023.

[3] Mikolajewicz et al., “Deglaciation and abrupt events in a coupled comprehensive atmosphere-ocean-ice sheet-solid earth model”, Climate of the Past Discussions, 1–46, 2024.

[4] Racky et al., “PaleoPlaSim 1.0: An Earth System Model of Intermediate Complexity for Paleoclimate Modeling and Large Ensemble Studies”, in prep., Proceedings of the 11th bwHPC Symposium 2025, 2025.

[5] Fraedrich et al., “The Planet Simulator: Towards a user friendly model”, Meteorologische Zeitschrift, 299–304, 2005.